We use the metrics defined in [1]}, including mean absolute error (abs), mean absolute relative error (abs-rel), squared relative error (sq-rel), RMSE in linear (rmse) and log (rmse-log) scales, and inlier ratios under thresholds of 1.25/1.25\(^2\) /1.25\(^3\) . For a predicted depth map \(y\)  and ground truth \(y^*\) , each with \(n\)  pixels indexed by \(i\) , those metrics are formulated as:
\(& \text{{\fontfamily {<qcr>}\selectfont abs}}: \frac{1}{n} \sum _{i} | y_i - y_i^*| \\& \text{{\fontfamily {<qcr>}\selectfont abs-rel}}: \frac{1}{n} \sum _{i} | y_i - y_i^*|/ y_i^* \quad \text{{\fontfamily {<qcr>}\selectfont sq-rel}}: \frac{1}{n} \sum _{i} \Vert  y_i - y_i^* \Vert ^2 / y_i^* \\& \text{{\fontfamily {<qcr>}\selectfont rmse}}: \sqrt{\frac{1}{n} \sum _{i} \Vert  y_i - y_i^* \Vert ^2} \\& \text{{\fontfamily {<qcr>}\selectfont rmse-log}}: \sqrt{\frac{1}{n} \sum _{i} \Vert  \log {y_i} - \log {y_i^*} \Vert ^2} \\& \text{inlier {\fontfamily {<qcr>}\selectfont ratio}} \% \text{ of } y_i \quad \text{ s.t. } \text{max} \left( \frac{y_i}{y_i^*}, \frac{y_i^*}{y_i} \right) = \delta < 1.25/1.25^2/1.25^3\) 
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